jev-browser-use
Use when driving a web page in a browser — clicking, typing, navigating, logged-in or…
Use when a transcript has to be cut to a fixed size and you must choose which turns go. Jev marks each turn keep, summarize or drop. Measured: it does not make a handoff better.
$ npx -y skills add kerpopule/hermes-jev-skills --skill jev-compaction --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/jev-compactionContext preview
The summary Claude sees to decide when to auto-load this skill.
Use when a transcript has to be cut to a fixed size and you must choose which turns go. Jev marks each turn keep, summarize or drop. Measured: it does not make a handoff better.
name: jev-compaction
description: Use when a transcript has to be cut to a fixed size and you must choose which turns go. Jev marks each turn keep, summarize or drop. Measured: it does not make a handoff better.
version: 0.2.0
license: MIT
metadata:
hermes:
tags: [jev, typesafe, compaction, handoff, context]Jev cannot write a summary. It can mark each turn of a transcript:
It judges a long turn on its first 350 and last 350 characters, redacted, 40 turns per request, and sees no other turn while it does.
This skill used to say a handoff written from Jev's digest "stops losing the one line that mattered". We measured that on seven real sessions and 104 recall questions ([scorecard](../../evals/compaction/results/SCORECARD-2026-09-20.md)) and it was wrong:
| the writer reads | capsule | recall alone | with one search of the old session | |---|---|---|---| | Jev's digest | 400 words | 37.5% | 68.3% | | the plain last 24,000 characters | 400 words | 48.1% | 68.3% | | **the whole dialogue** | **1,200 words** | **58.7%** | **75.0%** | | nothing: no handoff at all | | | 56.7% |
Jev's marks did beat the same marks handed out by recency (11 questions to 4), so the judgement is real. The digest built around it clips every other turn to 400 characters, and that cost more than the judgement earned. Nous Research found the same shape with a different Jev design ([hermes-agent PR 116246](https://github.com/NousResearch/hermes-agent/pull/116246)).
So, for a handoff:
1. **Give the writer the whole dialogue.** A current flash model reads 100,000 characters for about a cent. Do not pre-filter it. 2. **Ask for up to 1,200 words**, five headings: Working on, State, Decisions, Pointers, Next. At 400 words the capsule was full whatever the writer had read. 3. **Name the session in the handoff and say it is searchable.** One search was worth 16 to 33 points to every handoff we tried, and a session with no handoff and one search beat every handoff without one. On Hermes: `session_search(query="...")`, then `session_search(session_id=..., around_message_id=...)`. Passing `query` together with `session_id` ignores the query. 4. Do not append a list of "identifiers seen". It looked free and obvious; it changed nothing with a handoff and cost 13 points without one.
The `hermes-handoff` plugin does all four. `HANDOFF_JEV=1` puts the Jev pre-pass back if you want to compare on your own sessions with `evals/compaction/run_eval.py`.
When the size is fixed and something has to go: a small local writer, a context you cannot grow, a digest for a person to skim. There, choosing turns with Jev beat choosing them by recency.
1. Get the transcript as a list of `{role, content}` messages. On Hermes: `hermes sessions export --session-id <id> --format jsonl -`. 2. Select:
jev compact-select --digest < transcript.json # {"messages":[...]} or a bare list3. Write from `digest`. `[KEEP VERBATIM]` lines go in unchanged. `[background]` lines are clipped already; treat them as context, not as the record. 4. The digest is cut to its last 24,000 characters by default, oldest first, keep lines included. Pass a larger `limit` if early keep lines matter.
`should_compact` is arithmetic, not a model call: compact at 60% of the window, urgently at 85%. Do not ask a model whether the window is full.
Give your agent a fast, cheap second brain for the small decisions. Your agent burns frontier-model tokens on things that are not thinking: which model should answer this turn, which of 377 skills to load, which retrieved passages matter, which turns survive
Repo: kerpopule/hermes-jev-skills
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